Update app.py

app.py

@@ -4,70 +4,46 @@ import tensorflow as tf
 from PIL import Image
 import numpy as np
 from huggingface_hub import login, hf_hub_download
-import zipfile  # Added for extracting zip files
 
 # Authenticate with Hugging Face token (if available)
-hf_token = os.environ.get("HF_TOKEN")
+hf_token = os.environ.get("HF_TOKEN") 
 if hf_token:
     login(token=hf_token)
 
 # Download and load the model from the Hugging Face Hub
 repo_id = os.environ.get("MODEL_ID", "willco-afk/tree-test-x")  # Get repo ID from secret or default
-filename = "your_trained_model_resnet50.keras.zip"  # Updated filename
+filename = "your_trained_model.keras"  # Updated filename
 cache_dir = "./models"  # Local directory to cache the model
-os.makedirs(cache_dir, exist_ok=True)
-
-# Download the model file from Hugging Face
+os.makedirs(cache_dir, exist_ok=True)  
 model_path = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache_dir)
 
-# Extract and load the model
-model_unzipped_path = os.path.join(cache_dir, "your_trained_model_resnet50.keras")  # Path where we will extract the model
-if not os.path.exists(model_unzipped_path):
-    with zipfile.ZipFile(model_path, 'r') as zip_ref:
-        zip_ref.extractall(cache_dir)
-    print(f"Model unzipped to {model_unzipped_path}")
-
 # Load the model
-model = tf.keras.models.load_model(model_unzipped_path)
-
-# Function for image prediction
-def predict_decoration(image: Image.Image):
-    # Preprocess the image to match the model input size
-    image = image.resize((224, 224))  # Resize to match model's expected input size
-    image_array = np.array(image) / 255.0  # Normalize image to [0, 1]
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
-
-    # Make prediction
-    prediction = model.predict(image_array)
-    return "Decorated" if prediction[0][0] >= 0.5 else "Undecorated"
+model = tf.keras.models.load_model(model_path) 
 
 # Streamlit UI
-st.title("🎄 Christmas Tree Classifier 🎄")
+st.title("Christmas Tree Classifier")
 st.write("Upload an image of a Christmas tree to classify it:")
 
-# Create tabs
-tab1, tab2 = st.tabs(["Christmas Tree Classifier", "Sample Images"])
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
-# Tab 1: Christmas Tree Classifier
-with tab1:
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-    
-    if uploaded_file is not None:
-        # Display the uploaded image
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Uploaded Image.", use_container_width=True)
-
-        st.write("Classifying...")
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    # Updated Line:
+    st.image(image, caption="Uploaded Image.", use_container_width=True)  
+    st.write("")
+    st.write("Classifying...")
 
-        # Get prediction
-        predicted_class = predict_decoration(image)
-        st.write(f"Prediction: {predicted_class}")
+    # Preprocess the image
+    image = image.resize((224, 224))  # Resize to match your model's input size
+    image_array = np.array(image) / 255.0  # Normalize pixel values
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
 
-# Tab 2: Sample Images (with text and links only)
-with tab2:
-    st.header("Sample Images for the Model")
-    st.write("View some of my decorated and undecorated tree samples for the Model here:")
-    st.write("[Dropbox Link for Viewing Samples](https://www.dropbox.com/scl/fo/cuzo12z39cxv6joz7gz2o/h?rlkey=w10usqhkngf2uxwvllgnqb8tf&st=ld22fl4c&dl=0)")
+    # Make prediction
+    prediction = model.predict(image_array)
+    
+    # Get predicted class
+    predicted_class = "Decorated" if prediction[0][0] >= 0.5 else "Undecorated"
 
-    st.write("Download the tree sample images to test them on the model yourself here:")
-    st.write("[Dropbox Link for Downloading Samples](https://www.dropbox.com/scl/fo/cuzo12z39cxv6joz7gz2o/h?rlkey=w10usqhkngf2uxwvllgnqb8tf&st=ld22fl4c&dl=1)")
+    # Display the prediction
+    st.write(f"Prediction: {predicted_class}")